Data Engineering

50 Years of Data Warehousing: Is It Still Relevant?

In recent years, enterprise data architectures have evolved significantly to accommodate the changing data requirements of modern businesses. The emergence of advanced data storage technologies, such as cloud computing, data hubs, and data lakes have led us to wonder if traditional data warehousing is being pushed to the wayside for emerging solutions. 


What is data warehousing?

Data warehousing is the process of collecting and managing data from varied sources to provide meaningful business insights. Popularized during the nineties, data warehousing started as a common practice to collect and integrate data with the intent to create a consistent version of the truth for the organization that is harvesting and storing the data. Many organizations rely on data warehousing, considering them to be a critical asset used in many daily processes. 


Data warehousing works by collecting and organizing data into a comprehensive database. Once the data is collected, it's sorted into various tables decided upon by the data type and layout. Information that is then derived from the data warehouse can be used to help organizations analyze their consumers and predict trends in the competitive market. By having access to this centralized information, it becomes easier to provide exceptional customer experiences. 


What is data warehousing used for? 

Data warehousing has two primary functions. The first being to serve as a historical repository for integrating the information and data that is needed by the business and the second being to serve as a query execution and processing engine for all data. As a data management system, the main function of a data warehouse is to enable and support business intelligence by performing queries and analysis based on large amounts of historical data. Traditionally, data warehouses are housed on an enterprise server however, cloud-built and hybrid cloud data warehouses have become increasingly more popular. 


Data warehousing: a timeline 

In the last decade, data warehousing has undergone significant changes which can largely be attributed to the increasing popularity of cloud computing and big data technologies. Cloud-based data warehousing systems such as Snowflake, Amazon Redshift and Google BigQuery have emerged as popular alternatives to traditional on-premises data warehousing solutions. These emerging cloud-based systems offer increased scalability and flexibility as well as lower costs when compared to traditional data warehousing solutions. 


To understand how data warehousing has changed, it’s important to start from the beginning. The first data warehousing systems emerged in the 1970s and 80s, primarily being used by large organizations like government agencies and corporations. It wasn’t until the 1990s where a new generation of data warehousing was born. During this time, the introduction of the relational database management system (RDBMS and the development of the SQL language was created, enabling more flexible querying and reporting, making it easier for non-technical users to access and analyze data. During this time, notable companies like NCR, Oracle, IBM, and Microsoft established themselves as key players in the data warehousing market. 


Fast forward to the early 2000s when data warehousing systems began to evolve with the emergence of new technologies such as online analytical processing (OLAP) and data mining. These systems enabled deeper analysis and more sophisticated insights into data and began to be leveraged in different industries like marketing, healthcare, and finance. 


As the need to handle increasingly diverse and complex data sources continues to progress, data warehousing systems have followed suit. The last decade has seen significant changes in the field of data warehousing due to the emergence of new technologies and the increasing volume, velocity, and variety of data being generated. 


How has data warehousing changed over the last decade? 

Driven by the desire to provide faster, more flexible, and more accessible data warehousing solutions, data warehousing has progressed significantly over the last decade. Here are some of the ways in which data warehousing has changed over the last decade: 


  1. Cloud-based data warehousing 

One of the major shifts in data warehousing in recent years is the mass adoption of cloud-based data warehousing. Cloud-based data warehousing systems such as Snowflake, Redshift and Google BigQuery have become increasingly more popular due to their ability to provide flexible and scalable data warehousing solutions that can be easily accessed from anywhere. With massive scalability from virtualized cloud servers, these new platforms have proven to be able to handle the exponentially growing volume and variety of data.


  1. Big data technologies

In an attempt to keep up with the emergence of big data technologies like Hadoop, Spark, and NoSQL databases, data warehousing solutions have had to adapt to handle the processing and analysis of large volumes of unstructured and semi-structured data. This has led to the development of data warehousing systems that can handle diverse data sources and support advanced analytics. 


  1. Data lakes

Data lakes are large repositories or raw data that can be used for a variety of purposes including data warehousing. The concept of data lakes has risen in popularity in recent years largely due to their ability to enable organizations to store data in its original format which can later be processed and transformed if necessary. Data lakes are a crucial element in an analytics architecture allowing for exports to various data consuming applications.


  1. Real-time data warehousing 

Real-time data warehousing has become increasingly more important as organizations continue to leverage recent data to remain nimble and agile. Real-time data warehousing systems provide immediate insights that can be leveraged to make quick decisions based on recent data. Real-time source can be used by AI systems to provide best next step recommendations for ecommerce or deliver immediate access for business applications such as customer care.


  1. Self-service analytics 

Self-service analytics tools have risen in popularity in recent years largely because they enable anyone, regardless of their technical background, to access data and make informed business-decisions accordingly. This means employees can access and analyze data without relying on IT departments, increasing agility and faster decision-making. Salesforce Tableau has led the way in helping business users become analysts and with Microsoft PowerBI, analytics have been integrated even into Excel and Powerpoint.


What is the future of data warehousing? 

Despite technological advancements and emerging data warehousing solutions, the need for data warehousing remains constant. With the adoption of new technologies and the development of new approaches, data warehousing will continue to evolve to meet the growing needs of data-driven insights to drive business success. The following are key trends that are likely to shape the future of data warehousing:


  1. Cloud-Native Data Warehousing

Cloud-native data warehousing is expected to become even more popular as organizations continue to adopt cloud-based technologies. This will enable organizations to store and process massive amounts of data in the cloud, without having to manage the underlying infrastructure.


  1. Hybrid Data Warehousing

As data complexities continue to evolve, and flexibility and scalability continue to be top of mind, hybrid data warehousing is expected to become more popular. The process of hybrid data warehousing involves combining data warehousing solutions across multiple platforms including on-premises and cloud based systems. Hybrid models are particularly interesting when data such as consumer personal information has to be stored in geographically diverse locations due to local compliance requirements.


  1. Data Mesh

Data mesh is a relatively new approach to data warehousing that emphasizes decentralization and domain-driven design. This process involves breaking down data silos and enabling each team to manage its own data domain, while still ensuring data consistency and quality across the organization.


  1. Machine Learning and AI

Machine learning and AI are expected to play an increasingly important role in data warehousing by enabling organizations to uncover hidden patterns and insights that may have otherwise been missed. This will require the integration of machine learning and AI tools into data warehousing solutions, as well as the development of new algorithms and models.


  1. Real-Time Analytics

Real-time analytics are becoming increasingly more important as a competitive business landscape continues to demand perpetual innovations. Real-time data warehousing solutions will enable organizations to process and analyze data in real-time, providing immediate insights that can be used to make business decisions.


How will you leverage innovative data warehousing solutions that hone in on agility and accessibility? 

As emerging data warehousing solutions continue to arise, agility and accessibility continue to remain at the forefront. As long as organizations continue to need a centralized repository for maintaining historical data, the need for data warehousing will remain.

Ready to Asssess Your Data Environment?

Get in touch
Share this post